#30dayMapChallenge 2020

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#30dayMapChallenge

What is the 30dayMapChallenge?

My results 🎉

The idea is to make 30 maps from each day from 1st Nov to 30th Nov 2020. I hope to get about half done - so 15 maps.

I want to use this challenge to play around with different R packages for geospatial data and try out different ways of interactive visualizations.

GitHub Repo

Day 1: Points

Amount of cars in XHain mapped as points onto streets

There are 80.808 cars registered in Friedrichshain-Kreuzberg (2017).

If all cars would start driving at the same time - it’d get pretty crowded 🚗🚗 🚗

Click here for full map

The polygon of the streets are derived as a ‘negative’ from the official blocks provided by the Geoportal Berlin. Points to represent cars are sampled randomly within the polygon.

R source code

Data:

• Amount of cars via kleine Anfragen
• Polygon of streets derived from Geoportal Berlin ‘Blockkarte 1:5000 (ISU5)’

Tools & Packages:

• sf and mapdeck R package

Day 2: Lines

All domestic German flights 2019

About 10% of all flights from German airports are domestic flights - so starting in Germany and landing in Germany.

The amount of flights between German airports are mapped here:

Click here for full map

R source code

Data:

• Eurostat, the Statistical Office of the European Union, Air passenger transport between the main airports of Germany

Tools & Packages:

• geocode airports: tmaptools R package
• viz: flowmap.blue R package

Day 3: Polygons

The catchment area of boulder gyms in Berlin

Playing around with Voroni maps: “Voronoi polygons are created so that every location within a polygon is closer to the sample point in that polygon than any other sample point.”

Here: All boulder gyms (that I know of) in Berlin. If everyone would go his or her closest gym (by beeline), this would be the catchment areas of each boulder gym.

Click here for full map

R source code

Tools & Packages:

• geocode boulder gyms: tmaptools R package
• viz: mapview R package

Day 4: Hexagons

Traffic accidents in Stuttgart

The ‘Statistikportal’ offers a great data set on (almost) all accidents in Germany as single points. The mapdeck package auto aggregates point data into hexagons - so no need for data pre-processing.

I chose to crop the data to the outline of Stuttgart - but any other region or city can easily be used with the code by setting a different outline.

Click here for full map

R source code

Data:

• Unfallatlas | Statistische Ämter des Bundes und der Länder

Tools & Packages:

• get outline of Stuttgart: osmdata R package
• viz: mapview R package

Day 5: Blue

Day 6: Red

Rotpunkt

Today is another one on climbing - it’s less about the mapping tools.

In sport climbing, redpointing is free-climbing a route, while lead climbing, after having practiced the route beforehand. The English term “redpoint” is a loan translation of the German Rotpunkt coined by Kurt Albert in the mid-1970s at Frankenjura. He would paint a red X on a fixed pin so that he could avoid using it for a foot- or handhold. Once he was able to free-climb the entire route, he would put a red dot at the base of the route. In many ways, this was the origin of the free climbing movement that led to the development of sport climbing ten years later. Wikipedia

This map shows all notable ascents according to Wikipedia.

Click here for full map

R source code

Data:

Self compiled data set using:

• Wikipedia
• Coordinates mostly collected from thecrag.com

Day 7: Green

Day 8: Yellow

Hours of sunshine in Germany 2019

Where in Germany was a lot of sunshine in 2019 - where was it rather grey?

Click here for full map

R source code

Data:

• DWD Climate Data Center (CDC), Jahressumme der Raster der monatlichen Sonnenscheindauer für Deutschland, Version v1.1.

Tools & Packages:

• reading ESRI ascii grid file format: sp R package
• viz of raster data: leaflet R package
• hover information for raster data: leafem R package

Day 9: Monochrome

The life lines of Berlin

A fast way to find major streets within a city, without searching for any data on traffic amounts, street types or street width:

Take random start and end points within the city and run a routing to find routes connecting the start and end points.

Then aggregate the single street segments on how often they were used. You then get an image of the major city axes.

Big thanks to the stplanr package, which makes this easily done within a few lines of code!

Click here for full map

R source code

Tools & Packages:

• routing and aggregating line segments: stplanr R package
• viz: leaflet R package

Day 10: Grid

Which cuisine can you eat where in Berlin?

Click here for full map

R source code

Tools & Packages:

• Data on restaurants: osmdata R package
• Viz: mapdeck R package

Data:

OpenStreetMap via osmdata package

Day 11: 3D

Mapping the alpes in 3D

Playing around with the mapdeck::add_terrain() function. Not perfect yet, but works for a first try.

Click here for full map

R source code

Data:

Elevation data with Mapbox Tiles: elevation texture

Tools & Packages:

• Viz: mapdeck R package

Day 12: Map not made with GIS software

Day 13: Raster

Day 14: Climate change

Day 15: Connections

Day 16: Island(s)

Day 17: Historical map

Day 18: Landuse

Day 19: NULL

Day 20: Population

Day 21: Water

Day 22: Movement

Day 23: Boundaries

Day 24: Elevation

Day 25: COVID-19

Day 26: Map with a new tool

Day 27: Big or small data

Day 28: Non-geographic map

Day 29: Globe

Day 30: A map